1 Centralized risk management system: In this system, the responsibility of risk management is put at the senior management level where it is supposed to belong.. •In centralized system,
Trang 1Reading 27 Risk Management
–––––––––––––––––––––––––––––––––––––– Copyright © FinQuiz.com All rights reserved ––––––––––––––––––––––––––––––––––––––
Risk management is considered to be a critical
component of the investment process Risk
management is not only hedging risk rather, it involves
managing risk i.e reducing, increasing, avoiding risk
exposures etc
Risk management is a continuous process that involves:
1 Proper identification of risks (i.e all callable bonds
have call risk)
2 Identification of the firm’s desired level of risk i.e
determining risk tolerance
3 Measurement of risks (i.e how to measure risk e.g
duration to measure interest rate risk, beta to measure
risk of stocks)
4 Monitoring and adjusting the exposures to align
actual risk exposures with desired target levels
Risk should be taken in those areas in which business has expertise and competitive advantage (in order to earn profits)
NOTE:
• Some risks are preferred to be taken on a regular basis, some should be taken occasionally and some should be avoided altogether
• The execution of transactions for managing risk is also a distinct process e.g for portfolios, it involves trade identification, pricing and execution
Risk governance is a process of setting risk management
policies and standards for an organization The risk
management process should be overseen by the senior
management who is responsible for all organizational
activities The quality of risk governance is determined by
its transparency, accountability, effectiveness
(achieving objectives), and efficiency (economical use
of resources to achieve objectives) Risk governance is
an important part of corporate governance
Risk governance structure can be centralized or
decentralized
1) Centralized risk management system: In this system,
the responsibility of risk management is put at the senior
management level where it is supposed to belong It is
also known as Enterprise Risk Management (ERM)
Advantages:
•Allows economies of scale
•Allows firm to recognize offsetting nature of
different risk exposures
•It considers risk exposures both in isolation and at
portfolio level
•In centralized system, risk management
responsibility is on a level closer to senior
management who are actually responsible for
managing it
•It provides an overall picture of the company’s risk
position
NOTE:
It is important to note that due to less than perfect correlation between risk exposures, overall risk is less than the individual risks
2) Decentralized system: In this system, risk management responsibility is placed on individual business unit managers Each unit calculates and reports its exposures independently
Advantage:
It allows people closer to the actual risk taking to directly manage it
Disadvantage:
It does not take into account portfolio effects across different units
Enterprise Risk Management (ERM):
It is a centralized risk management system in which there
is a firm-wide perspective on risk Effective ERM system typically incorporates the following steps:
1 Identify risk exposures of the company
3 Estimate risks
4 Identify overall risk exposures of the firm as well as the contribution of each risk factor to overall risk
5 Report risks periodically to senior management by establishing a proper process and determine capital allocation, risk limits and risk management policies
6 Monitor compliance with policies and risk limits
Trang 2Benefits of Enterprise-wide Risk Management (ERM):
1)It facilitates to put all firm’s risk on a comparable basis
2)It allows managing risk in diversified and global firms
3)It promotes discipline of collecting, storing and
analyzing all positions both individually and at
firm-wide level
4)It helps in detecting fraud
5)It provides risk information to stakeholders
6)It facilitates firms to be more flexible as decisions are
based on risk-return trade-off
Main characteristics of an ERM system:
• Centralized data warehouse: It collects and stores
data in a technologically efficient manner from all
business units
• Risk analysis i.e market risk (using parametric,
historical, Monte Carlo), stress testing, credit risk,
liquidity risk
• Decision making i.e reporting risk information to
stakeholders and adjusting risks to a desired level Some risk governance concern of investment firms:
• The risk manager is responsible for monitoring risk levels for all portfolio positions and portfolio as a whole and controlling the level of risk
should work together with the trading desks in the development of risk management specifications
• For an effective risk governance system, the back office of an investment firm must be fully
independent from the front office
Following are the categories of risks:
1 Financial Risks:
Risks that are derived from events in the external
financial markets
They include:
in firm or portfolio values These risks are linked to supply
and demand in various marketplaces It includes:
a)Interest rate risk
b)Exchange rate risk
c)Equity price risk
d)Commodity price risk
ii Credit risk: It is the risk of loss that arises when a
counterparty or debtor fails to perform or meet the
obligation on the agreed terms OTC derivatives (unlike
Exchange traded), are subject to credit risk as they
contain no explicit credit guarantee
iii Liquidity risk: It is the risk that arises when a financial
instrument cannot be purchased or sold without a
significant price impact i.e unwinding a position may
become costly or impossible
•This risk arises in both initiating and liquidating
transactions for both long and short positions but is
particularly serious for liquidating transactions
when there is a need to reduce exposure to avoid
large losses
•Liquidity risk is a serious problem and often is
difficult to observe and quantify
•Short squeezes: i.e start to buy in panic and price
keeps on rising; thus increasing losses
Derivatives do not help in managing liquidity risk because: They are usually no more liquid than the underlying
Indicators of liquidity:
liquidity for traded securities i.e the bid-ask spread widens when markets are illiquid However, bid-ask quotations can be applied when trades are of small size
b) Illiquidity ratio: It measures the price impact per $1
million traded in a day, expressed in % terms
transaction volume, the more liquid the instrument However, there is no certainty that historical volume patterns will repeat themselves
NOTE:
Funding risk refers to a risk associated with the availability
of cash
2 Non-financial risks:
It includes
i Operational risk: It is the risk of loss that arises from failures in the company’s operating systems and procedures or from external events due to technological factors, human errors, natural disasters etc
contracts (which involves a transfer of risk) because these risks do not have a developed derivative market
monitoring their systems, taking preventive actions, and having a plan in place to swiftly respond if any adverse event occurs
Trang 3ii Model risk: It is the risk that arises due to the use of
incorrect valuation model or misapplication of the
model
iii Settlement risk (or Herstatt risk):It is the risk that arises
when a counterparty defaults in its obligation while the
other counterparty is paying These payments can be
associated with the purchase and sale of cash securities
i.e equities and bonds along with cash transfers
executed for swaps, forwards, options and other types of
derivatives
have settlement risk because all transaction take
place between an exchange member and the
central counterparty (clearing house)
• Two-way payments involve settlement risk
because one party could owe payment to its
counterparty while that counterparty declares
bankruptcy and fails to make its payments
settlement risk
iv Regulatory risk: It is the risk associated with the
uncertainty of how a transaction will be regulated and
with the potential for regulations to change
• Regulated markets face the risk that the existing
regulatory regime may become harder, more
restrictive or more costly
• Unregulated markets face the risk of being
regulated which results in costs and restrictions
• Regulatory risk is difficult to estimate
• Equities, bonds, futures and exchange traded
derivatives markets usually are regulated at the
federal level, whereas OTC derivative markets and
transactions in alternative investments are loosely
regulated
• Regulatory risk and the degree of regulation vary
widely from country to country
• Regulatory risk is affected by the priorities of
politicians and regulators
• Derivatives may be regulated indirectly when they
are used by regulated companies
v Legal/contract risk: It refers to risk of loss arising that
arises when the legal system fails to enforce a contract
in which a firm has a financial stake
vi Tax risk: Tax risk arises because of the uncertainty
associated with tax laws i.e impact of level and type of
taxation
• E.g transactions exempt from taxation could later
be found to be taxable
• Equivalent combinations of financial instruments
do not have identical tax treatment
• Like regulatory risk, tax risk is affected by the
priorities of politicians and regulators
vii.Accounting risk: It arises from uncertainty associated
with recording and accounting rules regarding
transactions and risk of changes in these accounting rules and regulations
country
protecting proprietary information from competitors and adequately informing investors and the public
3 Sovereign/political risk:
Sovereign risk is a form of credit risk in which the borrower/debtor is the government of a sovereign nation
• It involves current and a potential credit risk
default and the estimated recovery rate
• It is relatively difficult to evaluate sovereign risk
• Risk evaluation involves evaluating debtor nation’s asset/liability/cash flow, willingness, alternative means of financing etc
Political risk: It is the risk associated with changes in the political environment i.e change in political regime or the potential impact of a change in party control in a developed nation
1) ESG Risk: It is the risk caused by environmental, social and governance factors
• Environmental factors: Environmental factors include decisions related to products & services i.e process of production etc
• Social factors: Social factors are related to company’s policies, practices regarding human resources, contractual arrangements and the workplace Risks include labor strikes etc
• Governance factors: These factors include corporate governance policies and procedures
2) Performance netting risk: This risk arises when firm’s incentive is based on net performance whereas there are asymmetric incentive fee arrangements with the portfolio managers This risk occurs only in multi-strategy, multimanager environments
Example:
• Manager A generated positive $10 million returns while manager B generated $10 million loss
• Firm’s net performance = 10 – 10 = 0
Practice: Example 2, Volume 5, Reading 27
Trang 4•Thus, firm does not get any incentive fee from the
client
•However, firm is required to pay manager A his
incentive fee
Solution: This risk can be managed by establishing
absolute negative performance thresholds for individual accounts
3) Settlement Netting risk: It refers to risk that arises when there are no “netting arrangements” i.e contract is based on “two-way payments”
•Volatility (represented by sigma σ) is measured by
S.D
•Volatility is preferred to describe portfolio risk for
portfolios that contain instruments with linear
payoffs
•Relative volatility: The volatility of the deviation of a
portfolio’s returns from benchmark portfolio returns
is known as active risk, tracking risk, tracking error
volatility or tracking error
Market risk has two dimensions:
1 Primary or first-order measures of risk:
Sensitivity of the assets to the factor (e.g duration)
These measures reflect the expected change in price of
a financial instrument for a unit change in the value of
another instrument
Examples:
and is a linear risk measure
•Duration for bonds
•Delta for options (measures option’s sensitivity to a
small change in the value of its underlying)
•Volatility (Vega) measures the change in the price
of an option for a change in underlying’s volatility
o Options are very sensitive to a change in
volatility due to their non-linear pay-off structure
o Swaps, futures and forwards are much less
sensitive to changes in volatility because they
have linear pay-offs
o Certain options may have risk associated with
correlation
•Time to expiration (theta) measures the change in
the price of an option for a change in time to
expiration (i.e 1 day reduction in its time to
expiration) Both theta and Vega are exclusively
associated with options
Change in the sensitivity to its respective factor
(sensitivity) e.g convexity
Examples:
•Convexity for fixed-income portfolios measures
how interest rate sensitivity changes with changes
in interest rates
• Gamma measures the delta’s sensitivity to a change in the underlying’s value
Value at Risk (VAR): is the minimum loss that would be exceeded with a specified probability in a specified period Equivalently, it is the maximum loss that will not
be exceeded with a specified confidence (1 – probability) in a specified period
Characteristics:
• VAR is considered as the financial service industry’s premier risk management technique
• It is expressed in currency (e.g dollar terms) or in percentage terms
can be applied to a portfolio of assets
• VAR represents a dollar value risk measure i.e
translates the volatility in portfolio value in dollar value unlike other measurements of risk i.e beta and standard deviation
Systematic (or Non-Diversifiable Risk)
• It is easily used to measure the loss from Market risk, but it involves complexity in measuring the loss from credit risk and other types of exposures
Example:
A one day VAR of $10mn using a probability of 5% means that there is a 5% chance that the portfolio could lose more than $10mn in the next trading day
Trang 5Three implications of this definition:
1 VAR measures minimum loss only i.e the actual loss
can be much greater than the specified amount
2 VAR is associated with a given probability i.e 5%, 1%
etc The lower the probability, the greater will be VAR
in magnitude
3 VAR is based on specified time period; thus it cannot
be compared directly for different time intervals
Generally, the longer the period, the greater is the
potential loss But mostly, longer time periods increase
VAR in a non-linear fashion
NOTE:
The objective of estimating VAR is to identify the
probability distribution characteristics of portfolio returns
5.2.1) Elements of Measuring Value at Risk
Three important decisions regarding VAR calculations:
1 Selection of an appropriate probability: typically 5%
or 1% is used
results in higher VAR
•Different probabilities provide identical information
for portfolios with linear risk characteristics
•No rule exists for selection of probability
2 Selection of an appropriate time period to match
turnover or reporting period: e.g
•Derivative dealers use one day
•Industrial firms use quarterly or annually
o The longer the period, the greater the VAR in
magnitude
3 Selection of an appropriate modeling technique i.e
analytical, historical method, Monte Carlo simulation
technique
Three Methods to Measure VAR
5.2.2) Analytical or variance-covariance method
(Delta Normal Method)
It assumes normally distributed portfolios The key to using
the analytical method is to estimate the portfolio’s
expected return and S.D of returns
VAR = E(R) – z-value (S.D)
To estimate Daily VAR, expected returns and S.D are adjusted as follows:
Daily E(R) = Annual E(R) / 250 Daily S.D = Annual S.D / √250 Similarly, other conversions include:
Monthly E(R) = Annual E(R) / 12 Monthly S.D = Annual S.D / √12 Daily E(R) = Monthly E(R) / 22 Daily S.D = Monthly S.D / √22
Advantages:
• It is easy to calculate
• It is easy to understand
• It allows to model the correlation of risks
• It can be applied to any time period according to industry custom
Disadvantages:
• It assumes normal distribution Portfolios that contain options are not normally distributed In addition, real life returns often exhibit leptokurtosis Therefore, it tends to give poor results for portfolios with non-normal return distributions
• It leads to understatement of actual magnitude and frequency of large losses for portfolios with excess kurtosis (fat tails)
• It is difficult to estimate correlation between individual assets in large portfolios
Implications of Using of Zero expected value in VAR estimation:
• It leads to greater VAR because expected returns are typically positive for longer time horizons
• It represents a more conservative approach as it leads to higher VAR
• It avoids the problem to estimate expected return since E(R) = 0
• It makes easier to adjust VAR for a different time period i.e short term VAR cannot be converted to long term VAR (or vice versa) * when average return is not zero
*Conversion:
Delta-Normal Method: The problem associated with non-normal distribution e.g in options can be solved by using option’s delta (delta = ∆ in option price / ∆ in
underlying’s price)
variable remains normally distributed when they are multiplied by a constant (i.e delta is constant here)
∆ in option price = ∆ in underlying price × delta
Trang 6• This trick converts the non-normal distribution of
option return into a normal distribution
• However, delta is appropriate to use only for small
changes in the underlying But when second-order
effects (i.e gamma) are used to improve results,
the relationship between the option price and the
underlying price begins to approximate the true
non-linear relationship This further creates problem
in using normal distribution assumption
5.2.3) Historical Method or Historical Simulation Method
This method uses actual historical returns from a
user-specified period in the recent past i.e plotting these
returns using a histogram or ranking these returns in
descending order e.g if there are 100 observations of
returns, 5% of 100 is 5 Thus, VAR at 5% probability will be
5th worst return Note that if nth return is not a discrete
number then average is taken
Key assumption: Future returns will be the same as actual
returns over some historical period
Example:
Total returns = 248 To calculate 5% VAR:
5% × 248 = 12 returns→ thus, VAR would be the 12th worst
return in the observations Assume that after
rank-ordering the data, the 12th worst return is -0.0294 If total
value of portfolio is $50 million, then one-day VAR would
thus be 0.0294 × $50,000,000 = $1.47 million
Advantages:
• It is a non-parametric approach i.e it does not
involve any assumption regarding probability
distribution
• It is easy to calculate and easy to understand
• It can be applied to any time period according to
industry custom
Disadvantages:
• It is based on historical data, which may not hold
in the future This problem is also included in other
two approaches
over time; therefore, it is inappropriate to base
results on historical data
Historical simulation: In this approach, current weights
are applied to a time-series of historical returns In this
method, the history of a hypothetical portfolio using the
current position is reconstructed
Example:
A 5 year duration bonds, after 1 year, will be of 4 year maturity Using historical simulation requires using a 4-year duration bond (probably held by someone else) NOTE:
Total VAR is not simply the sum of individual VARs because risks of individual positions are less than
perfectly correlated This is known as diversification
effect It is equal to:
Sum of individual VARs – Total VAR
5.2.4) Monte Carlo Simulation Method
It generates random outcomes according to assumed probability distribution and a set of input parameters It examines outcomes given a particular set of risks In this method:
• Random portfolio returns are generated
distribution
• From this distribution, it is determined that at which level the lower 5% (or 1%) of return outcomes occur
• This value is then applied to portfolio value to obtain VAR
Key assumption: common risk factors affect asset
returns
Important to note: Both Monte Carlo and analytical methods provide identical results when sample size is large i.e sample VAR converges to the true population VAR when sample size increases
Advantage:
It does not require normal distribution assumption i.e any distribution can be used
Disadvantage:
It involves making a large number of assumptions regarding inputs of the return distributions and their correlations
5.2.5) “Surplus at Risk”: VAR as It Applies to Pension Fund
Portfolios
protect the value of the fund surplus (plan assets – plan liabilities)
to the surplus by:
o Treating the liability portfolio as a short position
Practice: Example 6, Volume 5, Reading 27
Practice: Example 5,
Volume 5, Reading 27
Trang 7and
o Calculating VAR on the net position
pension fund managers
Advantages of VAR:
1)It measures total risk
2)It quantifies the potential losses in simple and easy to
understand terms
3)It is easily understood by senior management
4)It is a versatile measure of risk because it can be used
to compare performance of different units with
different risk characteristics
5)It can be used to allocate capital at risk
Disadvantages of VAR:
1)VAR is difficult to estimate
2)VAR ignores information given in the tails of loss
distribution i.e it does not tell the extent to which loss
can exceed
3)The VAR estimate is sensitive to the assumptions made
and to the method used Thus, different estimation
methods produce different values
4)It gives a false sense of security that risk is measured
and controlled properly
it may understate the magnitude and frequency of
losses
6)VAR is difficult to apply in complex organizations
7)Portfolio VAR is not equal to sum of VAR from
individual positions
8)It does not take into account the positive results into
its risk profile; thus, it provides an incomplete picture of
the overall exposures
9)VAR has an inherent limitation that distribution of past
changes in market risk factors cannot provide
accurate predictions of future market risk
Back-Testing: Back testing refers to tests performed to
evaluate whether VAR estimates prove accurate in
predicting results
•If the VAR is systematically “too low”, the model is
underestimating the risk and there will be too
many occasions where the loss in the portfolio
exceeds the VAR
•If the VAR is systematically “too high”, the model is
over estimating the risk and there will be frequent
changes in regulatory capital
Example:
Daily VAR at 5% is $1 million; then over 1 year, a loss of at
least $1 million is expected to exceed approximately
0.05 × 250 = 12.5 days If the results are quite different
from that the model predicts, then the model is
inappropriate and needs to be adjusted
Similarly, for the recent quarter, it is expected to exceed
= 0.05 × 60 = 3 days For recent month, it will be = 0.05 × 20 = 1 day
Incremental VAR: It is used to measure the incremental effect of an asset on portfolio VAR it incorporates the effects of correlation of an asset with the portfolio It is measured as follows:
Incremental VAR=Portfolio’s VAR including a specified asset – Portfolio’s VAR excluding that asset Cash Flow at Risk (CFAR): It represents minimum cash flow loss that is expected to be exceeded with a given probability over a specified time period
Earnings at Risk (EAR): It represents minimum earnings loss that is expected to be exceeded with a given probability over a specified time period
generate cash flows or profits/earnings but are not readily valued publicly
Tail Value at Risk (TVAR) or Conditional Tail Expectation = VAR + expected loss in excess of VAR when such excess loss occurs
• VAR objective is to quantify potential losses under normal market conditions
• Stress testing is used to analyze non-normal/unusual conditions that could result in higher than expected losses It involves the following two approaches:
5.5.1) Scenario Analysis
It is used to analyze portfolio under different scenarios
1 Stylized Scenarios:
It involves simulating a change in at least one factor i.e interest rate, exchange rate, stock price or commodity price relevant to the portfolio There are industry standards of stylized scenarios as well
Limitation:
In stylized method, shocks are applied in a sequential fashion; it does not take into account their simultaneous effects
2 Actual Extreme Events:
The analyst measures the impact of actual past extreme events on portfolio value
Trang 8Advantage:
It is useful to use when higher probability of extreme
event is expected relative to the probability given by
valuation models or historical time period
3 Hypothetical Events:
The analyst measures impact of events that never
happened in the past or were assigned small probability
in the past but can be expected to occur in future
Limitations of Scenario Analysis:
•Only provides information that loss will result in a
given scenario but does not provide the
probability of occurrence of that scenario
•Different estimation methods produce different
values
•It is difficult to identify the sensitivity of a portfolio’s
instruments to the designed scenarios
•It requires analyst to have good skill and expertise
Scenario analysis complements VAR because: VAR tells
the minimum loss with a specified probability (assuming
normal market conditions) but does not provide
information regarding unusual events and underlying
factors that would result in actual losses in excess of
specific amount
5.5.2) Stressing Models This involves examining how well a portfolio performs
under some of the most extreme market moves
•It involves analyzing a range of possibilities rather
than a single set of scenarios
•It is computationally more difficult to perform
1 Factor Push:
It involves pushing the prices and risk factors of an
underlying model in the most unfavorable way (that
indicates extreme risk climate) and analyzing their
combined effect on portfolio’s value
•It is used as a complement to VAR because it gives
actual loss in scenarios for which probability
estimation is difficult
•Limitation: It involves higher model risk
It involves mathematically optimizing the risk
variable/factor that will result in maximum loss to the
portfolio’s value
3 Worst case scenario analysis:
It involves analyzing the impact of worst cases on
portfolio’s value
Stress tests are used to supplement VAR because VAR
does not measure "event" (e.g., market crash) risk
Credit Risk: It is the risk associated with failure of counterparties to meet their obligations
There are two dimensions of credit risk:
1 Probability of default:
It refers to the probability that counterparty will default
on its obligation It is present within every credit-based transaction
It is expressed in terms of recovery rate i.e fraction of total amount that is owed
It is difficult to estimate credit risk compared to market risk because:
• Default events are infrequent
• There is lack of market data regarding such events
• The inability to determine the correlation between different credit events
There are two different time perspectives in credit risk:
1 Jump-to-default/ current credit risk:
It is the risk associated with immediate/current credit events i.e risk of not receiving payment that is currently due
2 Potential Credit Risk:
It is the risk associated with events that may occur in future i.e risk of not receiving future payment
Cross-default Provision: It refers to a provision according
to which if a borrower defaults on any outstanding credit obligations, the borrower ultimately defaults on all of them
Credit VAR: Credit VAR refers to maximum loss that is expected to occur over a specified period with specified confidence level e.g amount of credit loss that will not be exceeded in one year with 99% certainty
• In credit VAR the main focus is the upper tail unlike market VAR where focus is on the lower tail Credit VAR cannot be separated from market VAR due
to the fact that credit risk results from gains on market positions held
5.6.1) Option Pricing Theory and Credit Risk According to this theory, credit risk can be explained as follows:
A bond with credit risk can be viewed as: Default-free bond + implicit short put option
Trang 9• A put option is written explicitly by the bondholders
to the shareholders
• This put option gives shareholders the right to fully
discharge their liability by giving assets to
bondholders despite the fact that those assets
could be of less value relative to claim of
bondholders
5.6.2) Credit Risk of Forward Contracts:
Credit risk is faced by each party until contract is settled
i.e forward contract has no current credit risk
Market value of forward contract at a given time reflects
the potential credit risk
Market value indicates the amount of a claim that
would be subject to loss when credit default occurs
Value Long = Spot t – [Forward / (1 + r) n]
When counterparty declares bankruptcy before
contract expiration, then market value of a forward
contract at the time of bankruptcy (if positive)
represents the claim of non-defaulting counterparty
5.6.3) Credit Risk of Swaps
A swap is equivalent to a series of forward contracts
At each periodic payment, current credit risk exists
Market value of swaps at a given time reflects the
potential credit risk
In interest rate swaps and equity swaps, potential credit
risk is largest during the middle period of the swap’s
contract maturity period
In currency swaps, potential credit risk is largest during
the middle period and at the end of the life of the swap
due to exchange of notional amount at the termination
Swap ValueLong = PV inflows – PV outflows
1
!1 " # $ %&
When a party to which value is negative defaults → that
value represents claim of counterparty
When a party to which value is positive defaults → asset
with positive market value is held by the defaulting party
When counterparty defaults before a payment on swap
is due, the claim of creditor will be either the market
value at that time or the asset held by bankruptcy party
in bankruptcy proceedings
5.6.4) Credit Risk of Options:
Forward and swap contracts have bilateral credit risks Options have unilateral risks i.e the buyer of the option pays a cash premium at the initiation and owes nothing
to the seller of the option unless he decides to exercise the option
Options do not have current credit risk until expiration like forward contracts
American options have greater value because option holder has the right to exercise the option early
Market price of option represents the amount at risk When seller of an option defaults before option expiration, value of an option represents claim of option buyer
Value of the side held by the firm determines the treatment of derivative contract in bankruptcy:
• When value to firm is negative, it is the creditor’s claim
• When value to firm is positive, it is an asset of the firm
Derivatives credit risk v/s Loans credit risk: Credit risk of derivatives is smaller relative to credit risk of loans because:
• Unlike loans, derivatives e.g forwards, swaps have netting arrangements
• Unlike loans, most of the derivative contracts do not involve exchange of notional principal
amount; however, in case of default of counterparty, the amount owed to the defaulting party can serve as collateral
Liquidity adjusted VAR can be estimated to incorporate liquidity risk
Non-financial risks are difficult to estimate Therefore, these risks are managed by using insurance rather than measuring and hedging them
Practice: Example 8, Volume 5, Reading 27
Trang 106 MANAGING RISK
The key components of managing risk include:
1 Effective risk governance model i.e Policies which
• Determine overall responsibility of senior
management
• Effectively allocate resources among units
• Separate revenue generation activities from
controlling side of the business
2 Appropriate systems and technologies i.e
Methodologies used to implement policies
3 Sufficient and suitably trained personnel to evaluate
risk information and distribute this information to those
responsible for proper decision making
Principles of Effective Risk Management:
1)Return cannot be generated without taking risks
2)Transparency i.e risk should be fully understood
experienced people instead of mathematical
models
4)Assumptions used in the valuation models should be
critically analyzed
5)Proper communication of risks i.e risks should be
discussed openly
6)Risk should be diversified to obtain consistent returns
7)A disciplined approach should be followed i.e should
not take extreme positions
8)It is preferred to use common sense and be
approximately right instead of precisely wrong
9)Investment decisions should be based on risk-return
trade-off
6.1.1) Risk Budgeting Risk budgeting refers to allocating risk among units,
divisions, portfolio managers or individuals in an efficient
manner
• Risk capital is allocated by the firm before the fact
in order to provide guidance to the units, divisions
etc on the acceptable level of risk that a given
unit/division can undertake
• Generally, total sum of risk capital allocated to
individual units is greater than the risk budget of
the firm as a whole because of the impact of
diversification
• Risk budgeting is used to allocate funds to portfolio
managers according to their Information ratios
(IRs)
Note: It is recommended to use
correlation-adjusted IR (to evaluate manager’s ability to add
value) to eliminate the effect of asset class
correlations
NOTE:
• Return on capital = Profit ($) / Capital ($)
• Return on VAR = Profit ($) / VAR ($) → higher return
on VAR indicates that manager has outperformed
on a risk-adjusted basis
For Details refer to Reading 27, Curriculum, Volume 5
Ways to manage Credit Risk:
6.2.1) Reducing Credit Risk by Limiting Exposure Credit risk can be reduced/managed by limiting exposure to a single party e.g single broker
6.2.2) Reducing Credit Risk by Marking to Market
It involves recalculating forward or swap price after party to which value is negative pays out the party to
which value is positive It is important to note that option
contracts are not marked to market because in options value is always positive to one party of the contract Option credit risk is managed by using collateral
6.2.3) Reducing Credit Risk with Collateral
Credit risk can be reduced by requiring parties to a contract to post collateral
6.2.4) Reducing Credit Risk with Netting
By using netting arrangements credit risk can be reduced as it results in lower amount of money that must
be paid It is useful in reducing credit risk in the following cases:
• Close out netting: It refers to a situation when after
netting, defaulting party ultimately has a claim on
non-defaulting party (i.e in spite of being bankrupt, party has claim on other party) This scenario assumes that the non-defaulting party owes the defaulting party a greater amount
• Cherry picking: It refers to a practice when bankrupt party attempts to enforce favorable contracts while neglects non-profitable contracts
It is important to note that netting arrangements are effective only when they are recognized by the legal system
Practice: Example 10, Volume 5, Reading 27